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Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations

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In this presentation in WSOM 2012 conference, we introduce the concept of pathways of wellbeing
and examine how such paths can be discovered from large data
sets using the self-organizing map. Data sets used in the illustrative experiments
include measurements of physical fitness and subjective assessments
related to diagnosing work stress. In addition, we show results from related projects.

Published in: Technology
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Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations

  1. 1. Paths of Wellbeing on Self-Organizing MapsKrista Lagus Aalto University (former Helsinki University of Technology)Tommi Vatanen Sports Institute of FinlandOili Kettunen StressinmurtajatAntti Heikkilä National Consumer Research CenterMatti HeikkiläMika Pantzar FinlandTimo Honkela
  2. 2. Motivation for Wellbeing informatics• World health situation: • WHO alarms of a stress epidemic: top 5 debilitating diseases are related to stress• Challenge: General advice affects individuals poorly > need customized lifestyle solutions
  3. 3. Ongoing work: VirtualCoach project PI: Krista Lagus Question sets Themes ”Appreciative inquiry” mental wellbeing, stress & relaxation loneliness & socialSocial media wellbeing application Explorative physical fitness data analysis: nutrition and food sleep paths of work and life wellbeing
  4. 4. Wellbeing data collections and analysis Illness & disease Doctors research Coaches, Research on peers, wellbeing and social lifestylesOUR networksFOCUS
  5. 5. ”classical example” SOM of wellbeing factors among Finnish youth(Honkela, Koskinen, Koskenniemi & Karvonen, 2000)
  6. 6. Sports Institute of Finland (Vierumäki) fitness data >100,000 measurements in 20+ yearssmall subset with also mental workload & stress evaluation example: abdominalsWhat kind of males femalesdifferent ”fitness allgroups” can befound?Relationshipbetweenphysical & mental 40-50wellbeing (stress)? years oldDo interventionshelp? (Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)
  7. 7. Sports Institute of Finland (Vierumäki) fitness data >100,000 measurements in 20+ yearssmall subset with also mental workload & stress evaluation example: abdominalsWhat kind of males femalesdifferent ”fitness allgroups” can befound?Relationshipbetweenphysical & mental 40-50wellbeing (stress)? years oldDo interventionshelp? (Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)
  8. 8. Map of fitness and stress
  9. 9. Individual wellbeing paths onthe map of fitness and stress
  10. 10. Methodological view: We need...● Big data on everyday life ● Quantative measurements ● Qualitative personal experiences● Methods for ● Dimensionality reduction ● Information visualization ● Time-series modeling ● Text mining ● Etc.
  11. 11. Identifying anomalous social contexts from mobile proximity data using binomial mixture models Eric Malmi, Juha Raitio, Oskar Kohonen, Krista Lagus, and Timo Honkela IDA 2012
  12. 12. ● Bluetooth data as an indicator of the social context● The data tells about the people and devices nearby● Period of time: 17 monts● Data on 106 people, at least 90 days each
  13. 13. Text mining for wellbeing: Selecting stories usingsemantic and pragmatic features Timo Honkela, Zaur Izzatdust, Krista Lagus ICANN 2012
  14. 14. Text mining for peer support Users User modelingDiscussion forum input and analysis of postings, etc. feedback (Honkela, Izzatdust, Lagus 2012) STYLE TOPIC ANALYSIS SENTIMENT ANALYSIS ANALYSIS MULTICRITERIA SELECTION PROCESS Selected stories EVALUATION
  15. 15. ICA of wellbeing-related terms in Reddit texts (Honkela, Izzatdust, Lagus 2012)
  16. 16. Subjects on objects in contexts:Using GICA method to quantify epistemological subjectivity Timo Honkela, Juha Raitio, Krista Lagus, Ilari T. Nieminen, Nina Honkela, and Mika Pantzar IJCNN 2012
  17. 17. Subjectifying: adding subjectiveviews into object-context matricesOutcome: Subject-Object-Context (SOC) Tensors
  18. 18. Potential sources for subjectification● Conceptual surveys: ● individual assessment of contextual appropriateness● Text mining: ● statistics of word/phrase-context patterns● Empirical psychology: ● reaction times, etc.● Brain research
  19. 19. Flattening: unfolding 3-way tensor for traditional 2-way analysis
  20. 20. Case 1: Wellbeing concepts CONTEXTS:OBJECTS:RelaxationHappinessFitnessWellbeingSUBJECTS:Event participants
  21. 21. MDS: Objects x Subjects Fitness
  22. 22. NeRV: Objects x Subjects FitnessNeRV:J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information Retrieval Perspective to NonlinearDimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.
  23. 23. SOM: Objects x Subjects
  24. 24. Case 2: State of the Union Addresses● In this case, text mining is used for populating the Subject-Object-Context tensor● This took place by calculating the frequencies on how often a subject uses an object word in the context of a context word ● Context window of 30 words
  25. 25. Analysis of the word health
  26. 26. Interactive SOMs: “Parametric modeling,non-parametric visualization”Timo Honkela and Michael Knapek Unpublished, ongoing work
  27. 27. E TL TI IV E Interactive SOMs: AT “Making the analysis process and N ERLT variable selection more transparent”A Timo Honkela and Michael Knapek Unpublished, ongoing work
  28. 28. Data points “chase” BMUs
  29. 29. Thank you! ¡Gracias!Kiitos! Merci! Obrigado!Danke schön! ありがとう

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